Neural Networks Predictive model

Model Deployment with PMML, the Predictive Model Markup Language
Strain, and current Muscle The idea behind this demo is to show you how easy it is to operationally deploy a predictive solution once it is represented in PMML, the Predictive Model Markup Language.
As a model building environment, I use KNIME to generate a neural network model for predicting customer churn. Once data pre-processing and model are represented in PMML, I go on to deploy it in the Amazon Cloud using the ADAPA Scoring Engine and on top of Hadoop using the Universal PMML Plug-in (UPPI) for Datameer. So, the very same model is readily available for execution in two very distinct Big Data platforms: cloud and Hadoop.
The easy of model deployment and interoperability between platforms is the power of PMML, the de facto standard for predictive analytics and data mining models.

Resources:

  1. Download the KNIME workflow used to generate a sample neural network for predicting churn
  2. Download the PMML file created during the demo

Source: Zementis Predictive Analytics

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